Add neg bit (#56058)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56058

User facing changes:
1. Adds a negative bit and corresponding new API (`is_neg()`,`resolve_neg()`)
2. `tensor.conj().imag` now returns a floating point tensor with neg bit set to 1 instead of a tensor with no notion of negative bit. Note that imag is still a view and all the view properties still hold for imag.

Non user facing changes:
1. Added a new Negative dispatch key and a backend fallback to handle it
2. Updated copy kernel to handle negative bit
3. Merged conjugate and negative bit fallback kernel
4. fixed https://github.com/pytorch/pytorch/issues/60478 (caused due to https://github.com/pytorch/pytorch/pull/54987)

Testing:
1. Added a new OpInfo based test `test_neg_view` (verifies that out-of-place and in-place operations work correctly for all operations when the input is a neg view tensor by checking the result against an actually negated tensor, verifies that autograd returns the same output for both neg view and actually negated tensors as well as it works fine when grad_out is a neg view).
2. Added a new test class containing `test_conj_view`, `test_neg_view`.

Test Plan: Imported from OSS

Reviewed By: soulitzer

Differential Revision: D29636403

fbshipit-source-id: 12214c9dc4806c51850f4a72a109db9527c0ca63

torch/_tensor_str.py

@@ -233,10 +233,16 @@ def _tensor_str(self, indent):
         self = self.rename(None)
 
     summarize = self.numel() > PRINT_OPTS.threshold
+
+    # handle the negative bit
+    if self.is_neg():
+        self = self.resolve_neg()
+
     if self.dtype is torch.float16 or self.dtype is torch.bfloat16:
         self = self.float()
 
     if self.dtype.is_complex:
+        # handle the conjugate bit
         self = self.resolve_conj()
         real_formatter = _Formatter(get_summarized_data(self.real) if summarize else self.real)
         imag_formatter = _Formatter(get_summarized_data(self.imag) if summarize else self.imag)